U.S. patent application number 16/479380 was filed with the patent office on 2019-12-19 for feedback for an autonomous vehicle.
The applicant listed for this patent is Ford Global Technologies, LLC. Invention is credited to Guy HOTSON, Jinesh J. JAIN, Maryam MOOSAEI, Vidya NARIYAMBUT MURALI.
Application Number | 20190382030 16/479380 |
Document ID | / |
Family ID | 62979548 |
Filed Date | 2019-12-19 |
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United States Patent
Application |
20190382030 |
Kind Code |
A1 |
HOTSON; Guy ; et
al. |
December 19, 2019 |
FEEDBACK FOR AN AUTONOMOUS VEHICLE
Abstract
A controller receives sensor data during a ride and provides it
to a server system. A passenger further provides feedback
concerning the ride in the form of some or all of an overall
rating, flagging of ride anomalies, and flagging of road anomalies.
The sensor data and feedback are input to a training algorithm,
such as a deep reinforcement learning algorithm, which updates an
artificial intelligence (AI) model. The updated model is then
propagated to controllers of one or more autonomous vehicle which
then perform autonomous navigation and collision avoidance using
the updated AI model.
Inventors: |
HOTSON; Guy; (Dearborn,
MI) ; MOOSAEI; Maryam; (Dearborn, MI) ;
NARIYAMBUT MURALI; Vidya; (Dearborn, MI) ; JAIN;
Jinesh J.; (Dearborn, MI) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Ford Global Technologies, LLC |
Dearborn |
MI |
US |
|
|
Family ID: |
62979548 |
Appl. No.: |
16/479380 |
Filed: |
January 24, 2017 |
PCT Filed: |
January 24, 2017 |
PCT NO: |
PCT/US2017/014769 |
371 Date: |
July 19, 2019 |
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
B60W 2420/52 20130101;
G05D 1/0088 20130101; B60W 30/02 20130101; B60W 30/18145 20130101;
B60W 2050/146 20130101; B60W 50/10 20130101; B60W 2552/00 20200201;
B60W 50/08 20130101; G06N 3/08 20130101; B60W 2540/215 20200201;
G05B 13/027 20130101; B60W 50/14 20130101; B60W 30/12 20130101;
B60W 50/02 20130101; B60W 2420/42 20130101; B60W 2554/00 20200201;
B60W 2050/0088 20130101 |
International
Class: |
B60W 50/02 20060101
B60W050/02; G05D 1/00 20060101 G05D001/00; B60W 30/18 20060101
B60W030/18; B60W 50/14 20060101 B60W050/14; G06N 3/08 20060101
G06N003/08; G05B 13/02 20060101 G05B013/02 |
Claims
1. A method comprising, by a computer system: receiving one or more
inputs from a passenger of an autonomous vehicle; receiving sensor
data from the autonomous vehicle; and updating control logic of the
autonomous vehicle according to the one or more inputs and the
sensor data to obtain updated control logic.
2. The method of claim 1, wherein the control logic of the
autonomous vehicle implements an artificial intelligence (AI)
model.
3. The method of claim 2, wherein updating control logic of the
autonomous vehicle comprises updating the AI model using a deep
reinforcement learning algorithm according to the one or more
inputs and the sensor data.
4. The method of claim 1, wherein the one or more inputs include a
report of a driving anomaly.
5. The method of claim 1, wherein the one or more inputs include a
report of a lane deviation.
6. The method of claim 1, wherein the one or more inputs include a
report of a deviation during a turn.
7. The method of claim 1, wherein receiving the one or more inputs
comprises receiving the one or more inputs from a mobile device of
the passenger.
8. The method of claim 7, wherein receiving the one or more inputs
comprises receiving a user selection of one or more locations on a
map displayed on the mobile device and an indication that the one
or more locations correspond to at least one of a road anomaly and
a driving anomaly.
9. The method of claim 1, wherein the sensor data includes outputs
of at least one of a light detection and ranging (LIDAR) sensor, a
radio detection and ranging (RADAR) sensor, and one or more
cameras.
10. The method of claim 1, further comprising: receiving, by a
controller of the autonomous vehicle, outputs of one or more
sensors; and autonomously driving, by the controller, the
autonomous vehicle using the outputs processed according to the
updated control logic.
11. A system comprising one or more processing devices and one or
more memory devices operably coupled to the one or more processing
devices, the one or more memory devices storing executable code
effective to cause the one or more processing devices to: receive
one or more inputs from a passenger of an autonomous vehicle;
receive sensor data from the autonomous vehicle; and update control
logic of the autonomous vehicle according to the one or more inputs
and the sensor data to obtain updated control logic.
12. The system of claim 11, wherein the control logic of the
autonomous vehicle implements an artificial intelligence (AI)
model.
13. The system of claim 12, wherein the executable code is further
effective to cause the one or more processors to update control
logic of the autonomous vehicle by updating the AI model using a
deep reinforcement learning algorithm according to the one or more
inputs and the sensor data.
14. The system of claim 11, wherein the one or more inputs include
a report of a driving anomaly.
15. The system of claim 11, wherein the one or more inputs include
a report of a lane deviation.
16. The system of claim 11, wherein the one or more inputs include
a report of deviation during a turn.
17. The system of claim 11, wherein the executable code is further
effective to cause the one or more processors to receive the one or
more inputs by receiving the one or more inputs from a mobile
device of the passenger.
18. The system of claim 17, wherein the executable code is further
effective to cause the one or more processors to receive the one or
more inputs by receiving a user selection of one or more locations
on a map displayed on the mobile device and an indication that the
one or more locations correspond to at least one of a road anomaly
and a driving anomaly.
19. The system of claim 11, wherein the sensor data includes
outputs of at least one of a light detection and ranging (LIDAR)
sensor, a radio detection and ranging (RADAR) sensor, and one or
more cameras.
20. The system of claim 11, further comprising the autonomous
vehicle comprising a controller, the controller being programmed
to: receive outputs of one or more sensors; and autonomously drive
the autonomous vehicle using the outputs processed according to the
updated control logic.
Description
BACKGROUND
Field of the Invention
[0001] This invention relates to operating an autonomous
vehicle.
Background of the Invention
[0002] Autonomous vehicles are becoming much more relevant and
utilized on a day-to-day basis. In an autonomous vehicle, a
controller relies on sensors to detect surrounding obstacles and
road surfaces. The controller implements logic that enables the
control of steering, braking, and accelerating to reach a
destination and avoid collisions.
[0003] The system and method disclosed herein provide an improved
approach for implementing control logic for an autonomous
vehicle.
BRIEF DESCRIPTION OF THE DRAWINGS
[0004] In order that the advantages of the invention will be
readily understood, a more particular description of the invention
briefly described above will be rendered by reference to specific
embodiments illustrated in the appended drawings. Understanding
that these drawings depict only typical embodiments of the
invention and are not therefore to be considered limiting of its
scope, the invention will be described and explained with
additional specificity and detail through use of the accompanying
drawings, in which:
[0005] FIG. 1 is a schematic block diagram of components
implementing a system in accordance with an embodiment of the
present invention;
[0006] FIG. 2 is a schematic block diagram of an example computing
device suitable for implementing methods in accordance with
embodiments of the invention;
[0007] FIG. 3 is a process flow diagram of a method for updating
control logic based on passenger feedback in accordance with
embodiments of the present invention;
[0008] FIG. 4 illustrates an interface for receiving passenger
feedback in accordance with an embodiment of the present
invention.
DETAILED DESCRIPTION
[0009] Referring to FIG. 1, the methods disclosed herein may be
performed using the illustrated system 100. As discussed in greater
detail herein, a controller 102 may perform autonomous navigation
and collision avoidance for a vehicle housing the controller 102.
The vehicle may have all of the structures and features of any
vehicle known in the art including, wheels, a drive train coupled
to the wheels, an engine coupled to the drive train, a steering
system, a braking system, and other systems known in the art to be
included in a vehicle.
[0010] The controller 102 may receive one or more outputs from one
or more exterior sensors 104. For example, one or more cameras 106a
may be mounted to the vehicle 100 and output image streams to the
controller 102. The exterior sensors 104 may include sensors such
as an ultrasonic sensor 106b, a RADAR (Radio Detection and Ranging)
sensor 106c, a LIDAR (Light Detection and Ranging) sensor 106d, a
SONAR (Sound Navigation and Ranging) sensor 106e, and the like.
[0011] The controller 102 may execute an autonomous operation
module 108 that receives the outputs of the exterior sensors 104.
The autonomous operation module 108 may include an obstacle
identification module 110a, a collision prediction module 110b, and
a decision module 110c. The obstacle identification module 110a
analyzes the outputs of the exterior sensors and identifies
potential obstacles, including people, animals, vehicles,
buildings, curbs, and other objects and structures. In particular,
the obstacle identification module 110a may identify vehicle images
in the sensor outputs.
[0012] The collision prediction module 110b predicts which obstacle
images are likely to collide with the vehicle 100 based on its
current trajectory or current intended path. The collision
prediction module 110b may evaluate the likelihood of collision
with objects identified by the obstacle identification module 110a.
The decision module 110c may make a decision to stop, accelerate,
turn, etc. in order to avoid obstacles. The manner in which the
collision prediction module 110b predicts potential collisions and
the manner in which the decision module 110c takes action to avoid
potential collisions may be according to any method or system known
in the art of autonomous vehicles.
[0013] The decision module 110c may control the trajectory of the
vehicle to navigate to a desired objective while avoiding
obstacles. For example, the decision module 110c may activate one
or more actuators 112 controlling the direction and speed of the
vehicle 100. For example, the actuators 112 may include a steering
actuator 114a, an accelerator actuator 114b, and a brake actuator
114c. The configuration of the actuators 114a-114c may be according
to any implementation of such actuators known in the art of
autonomous vehicles.
[0014] In embodiments, the above-described functionality of some or
all of the obstacle identification module 110a, collision
prediction module 110b, and decision module 110c may be implemented
by an artificial intelligence (AI) model 116. The AI model 116 may
be a machine learning model such as a deep neural network (DNN),
Bayesian machine learning model, or the like. In some embodiments,
the deep reinforcement learning algorithm provided by GOOGLE may be
used to generate the AI model 116.
[0015] The controller 102 may be in data communication with a
server system 118. For example, the controller 102 may be in data
communication with one or more cellular communication towers 120
that are in data communication with the server system 118 by way of
a network 122, such as a local area network (LAN), wide area
network (WAN), the Internet, or any other wireless or wired network
connection.
[0016] The server system 118 may host or access a database 124. The
database 124 may store ride reports 126. The ride reports 126 may
include user feedback 128a. The feedback 128a may be feedback
received from a passenger during or following a ride as described
below. For example, the autonomous operation module 108 may include
a feedback module 110d through which the passenger provides
feedback as described according to the method described below. The
ride reports 126 may further include sensor data 128b for each
ride, such as some or all of the sensor data for some or all of the
exterior sensors 104.
[0017] As described herein below, a user may provide feedback by
means of a mobile device 130, such as a mobile phone, tablet
computer wearable computer. The functions ascribed herein to the
mobile device 130 may also be performed by a desktop or laptop
computer or any other type of computing device. In some
embodiments, the mobile device 130 may communicate directly with
the server 118 or by way of the controller 102 or some other
intermediary computing device.
[0018] As described in greater detail below, the database 124 may
also include a version of the AI model 116. The AI model 116 may be
updated by the server system 118 in response to the ride reports
126 as described below. The AI model 116 as updated may be
transmitted to the controllers 102 of one or more vehicles to
replace previous versions of the AI model 116 accessed by the
controllers.
[0019] FIG. 2 is a block diagram illustrating an example computing
device 200. Computing device 200 may be used to perform various
procedures, such as those discussed herein. The controller 102,
server system 118, and mobile device 130 may have some or all of
the attributes of the computing device 200.
[0020] Computing device 200 includes one or more processor(s) 202,
one or more memory device(s) 204, one or more interface(s) 206, one
or more mass storage device(s) 208, one or more Input/Output (I/O)
device(s) 210, and a display device 230 all of which are coupled to
a bus 212. Processor(s) 202 include one or more processors or
controllers that execute instructions stored in memory device(s)
204 and/or mass storage device(s) 208. Processor(s) 202 may also
include various types of computer-readable media, such as cache
memory.
[0021] Memory device(s) 204 include various computer-readable
media, such as volatile memory (e.g., random access memory (RAM)
214) and/or nonvolatile memory (e.g., read-only memory (ROM) 216).
Memory device(s) 204 may also include rewritable ROM, such as Flash
memory.
[0022] Mass storage device(s) 208 include various computer readable
media, such as magnetic tapes, magnetic disks, optical disks,
solid-state memory (e.g., Flash memory), and so forth. As shown in
FIG. 2, a particular mass storage device is a hard disk drive 224.
Various drives may also be included in mass storage device(s) 208
to enable reading from and/or writing to the various computer
readable media. Mass storage device(s) 208 include removable media
226 and/or non-removable media.
[0023] I/O device(s) 210 include various devices that allow data
and/or other information to be input to or retrieved from computing
device 200. Example I/O device(s) 210 include cursor control
devices, keyboards, keypads, microphones, monitors or other display
devices, speakers, printers, network interface cards, modems,
lenses, CCDs or other image capture devices, and the like.
[0024] Display device 230 includes any type of device capable of
displaying information to one or more users of computing device
200. Examples of display device 230 include a monitor, display
terminal, video projection device, and the like.
[0025] Interface(s) 206 include various interfaces that allow
computing device 200 to interact with other systems, devices, or
computing environments. Example interface(s) 206 include any number
of different network interfaces 220, such as interfaces to local
area networks (LANs), wide area networks (WANs), wireless networks,
and the Internet. Other interface(s) include user interface 218 and
peripheral device interface 222. The interface(s) 206 may also
include one or more peripheral interfaces such as interfaces for
printers, pointing devices (mice, track pad, etc.), keyboards, and
the like.
[0026] Bus 212 allows processor(s) 202, memory device(s) 204,
interface(s) 206, mass storage device(s) 208, I/O device(s) 210,
and display device 230 to communicate with one another, as well as
other devices or components coupled to bus 212. Bus 212 represents
one or more of several types of bus structures, such as a system
bus, PCI bus, IEEE 1394 bus, USB bus, and so forth.
[0027] For purposes of illustration, programs and other executable
program components are shown herein as discrete blocks, although it
is understood that such programs and components may reside at
various times in different storage components of computing device
200, and are executed by processor(s) 202. Alternatively, the
systems and procedures described herein can be implemented in
hardware, or a combination of hardware, software, and/or firmware.
For example, one or more application specific integrated circuits
(ASICs) can be programmed to carry out one or more of the systems
and procedures described herein.
[0028] Referring to FIG. 3, the illustrated method 300 may be
executed by the server system 118 in cooperating with a mobile
device 130 of a passenger and the controller 102 of an autonomous
vehicle in which the passenger has traveled or is travelling.
[0029] The method 300 may include presenting 302 an interface to
the customer on the mobile device 130 for receiving feedback from
the passenger about the ride. The data for populating the interface
may be transmitted by the server system 118 to the mobile device
130. For example, the controller 102 may transmit data for a ride
to the server system 118 or directly to the mobile device 130. The
data for the ride may include the route traveled and one or more
vehicle signals, such as signals derived from the exterior sensors
104 during the ride.
[0030] The controller 102 or server system 118 may further define
the interface and provide it to the mobile device 130 for display,
such as in the form of a web page. Alternatively, the controller
102 or server system 118 may provide data defining the interface to
an application executing on the mobile device 130, which then
renders the interface on the mobile device 130.
[0031] In yet another embodiment, data for defining the interface
is provided by the controller 102 directly to the mobile device
130, such as over a wireless network connection.
[0032] FIG. 4 illustrates an example interface. The interface may
include a map 400 illustrating streets, landmarks, labels of
streets and landmarks, and any other information that may be
included in a map as known in the art. The map may be superimposed
over a satellite image of the area represented by the map as known
in the art.
[0033] The interface may include an interface element 404 that a
user may select in order to provide a rating of a ride, e.g. a
positive or negative rating, a selection of a value form 1 to N,
where N indicates no problems and 1 indicates a poor quality
ride.
[0034] The interface may include a rendering 402 of a path of the
vehicle during the ride superimposed on the map. The interface may
receive user inputs specifying locations 406, 408 at which
anomalies occurred during the ride. In some embodiments, the
interface may receive passenger specification of types of
anomalies. For example, interface element 410 may enable the
passenger to flag a location of a ride anomaly. For example,
following selection of interface element 410, a subsequent
selection on the path 102 may be interpreted as user specification
of a ride anomaly. The interface may further receive a user
specification of a type of the ride anomaly, i.e. an autonomous
action of the vehicle that the passenger feels was not well
executed, such as departure from a lane, a turn that was taken too
fast or that deviated from an appropriate path, or the like.
[0035] The interface may include an interface element 412 that
enables the passenger to specify that a selected point on the path
map 400 corresponds to a road anomaly, e.g. a pot hole, shut down
lane, road construction, blocked road, accident etc.
[0036] Referring again to FIG. 3, the method 300 may include
receiving 304, by the server system 118, feedback through the
interface presented at step 302, such as some or all of the
feedback described above with respect to the interface of FIG. 4.
The method 300 further includes receiving 306, by the server system
118, sensor data. This may include receiving, for some or all of
the exterior sensors 104, a set of sensor readings throughout the
ride by that sensor. Accordingly, step 306 may include receiving
some or all of a set images received from one or more camera 106as,
a stream of outputs of the ultrasonic sensor 106b, RADAR readings
from the RADAR 106c, a set of point clouds from the LIDAR sensor
106d, and a set of SONAR readings from the SONAR sensor 106e.
[0037] In some embodiments, the GPS (global positioning system)
coordinates of the vehicle 100 throughout the ride, e.g. a time
point and the GPS coordinate at that time point, may be received
from the controller 102 at step 306.
[0038] In some embodiments, data received at step 306 may include
outputs of the decision module 110c, i.e. actions invoked by the
decision module 110c, such as activations of the actuators 112.
Data received at step 306 may include data describing the locations
and/or relative velocity of obstacles detected by the obstacle
identification module 110a during the ride and the locations of
predicted collisions identified by the collision prediction module
110b during the ride.
[0039] The method 300 may further include training 308 a model
according to both of the feedback of step 304 and the sensor data
of step 306. For example, the model may be the AI model 116.
Various machine learning models enable the model to be repeatedly
trained using additional training data. For example, the AI model
116 may be a deep reinforcement learning model, such as that
provided by GOOGLE.
[0040] In this case, training 308 the model may include using the
sensor data as inputs and decisions of the decision module 110c as
outputs. In some embodiments, step 308 may include training the
model using tens, hundreds, or even thousands of data sets, where
each data set includes data from steps 304 and 306 of one ride.
Feedback is embodied as an overall rating of the passenger as well
as feedback about particular ride anomalies. As known in the art of
deep reinforcement learning models, the model may be trained based
on this feedback to promote actions that were rated highly by the
passenger, i.e. a highly rated and uneventful ride and reduce
occurrence of actions that are present in lowly rated rides or
flagged as anomalies by the passenger.
[0041] In one example, if during a certain section of the ride, the
passenger feels that the controller 100 causes the vehicle to take
an aggressive turn, the passenger will notify the server system 118
through feedback in the form of ratings, as described above.
Feedback may be received from multiple passengers, including
multiple passengers passing through the same turn and who provide
feedback. If lower ratings are received from multiple passengers
around that area, i.e. the server system 118 will train the model
116 using that feedback and the sensor data recorded around that
anomaly. With multiple such datasets over a period of time, deep
reinforcement learning helps to achieve a solution that maximizes
some sort of a cumulative reward. Thus, if the system received
various lower ratings for the aforementioned turn, using this
method, the model 116 would learn anomalies at the turn and
potentially make changes to the control strategies, which in case
might mean a more conservative turn or even a different route
altogether.
[0042] In another example, each ride taken by the passenger can be
represented as a sequence of state-action pairs, where the state of
the vehicle corresponds to sensor values and the action corresponds
to control outputs (e.g. the steering angle). Each batch of new
ratings with corresponding state-action sequences can then be used
within a deep reinforcement learning scheme such as a Deep-Q
Network. As new ratings are accumulated, the network will converge
upon a control policy that increases the ratings given by
passengers.
[0043] Following training, the AI model 116 as trained may then be
used to update 310 the controllers 102 of one or more vehicles. For
example, by transmitting the updated AI model to the controllers
102 over a network 122 and one or more cellular antennas 120 or by
a wired connection to the controller 102. The controllers 102 of
these vehicles may then perform obstacle avoidance and autonomous
navigation using the updated AI model 116.
[0044] In the above disclosure, reference has been made to the
accompanying drawings, which form a part hereof, and in which is
shown by way of illustration specific implementations in which the
disclosure may be practiced. It is understood that other
implementations may be utilized and structural changes may be made
without departing from the scope of the present disclosure.
References in the specification to "one embodiment," "an
embodiment," "an example embodiment," etc., indicate that the
embodiment described may include a particular feature, structure,
or characteristic, but every embodiment may not necessarily include
the particular feature, structure, or characteristic. Moreover,
such phrases are not necessarily referring to the same embodiment.
Further, when a particular feature, structure, or characteristic is
described in connection with an embodiment, it is submitted that it
is within the knowledge of one skilled in the art to affect such
feature, structure, or characteristic in connection with other
embodiments whether or not explicitly described.
[0045] Implementations of the systems, devices, and methods
disclosed herein may comprise or utilize a special purpose or
general-purpose computer including computer hardware, such as, for
example, one or more processors and system memory, as discussed
herein. Implementations within the scope of the present disclosure
may also include physical and other computer-readable media for
carrying or storing computer-executable instructions and/or data
structures. Such computer-readable media can be any available media
that can be accessed by a general purpose or special purpose
computer system. Computer-readable media that store
computer-executable instructions are computer storage media
(devices). Computer-readable media that carry computer-executable
instructions are transmission media. Thus, by way of example, and
not limitation, implementations of the disclosure can comprise at
least two distinctly different kinds of computer-readable media:
computer storage media (devices) and transmission media.
[0046] Computer storage media (devices) includes RAM, ROM, EEPROM,
CD-ROM, solid state drives ("SSDs") (e.g., based on RAM), Flash
memory, phase-change memory ("PCM"), other types of memory, other
optical disk storage, magnetic disk storage or other magnetic
storage devices, or any other medium which can be used to store
desired program code means in the form of computer-executable
instructions or data structures and which can be accessed by a
general purpose or special purpose computer.
[0047] An implementation of the devices, systems, and methods
disclosed herein may communicate over a computer network. A
"network" is defined as one or more data links that enable the
transport of electronic data between computer systems and/or
modules and/or other electronic devices. When information is
transferred or provided over a network or another communications
connection (either hardwired, wireless, or a combination of
hardwired or wireless) to a computer, the computer properly views
the connection as a transmission medium. Transmissions media can
include a network and/or data links, which can be used to carry
desired program code means in the form of computer-executable
instructions or data structures and which can be accessed by a
general purpose or special purpose computer. Combinations of the
above should also be included within the scope of computer-readable
media.
[0048] Computer-executable instructions comprise, for example,
instructions and data which, when executed at a processor, cause a
general purpose computer, special purpose computer, or special
purpose processing device to perform a certain function or group of
functions. The computer executable instructions may be, for
example, binaries, intermediate format instructions such as
assembly language, or even source code. Although the subject matter
has been described in language specific to structural features
and/or methodological acts, it is to be understood that the subject
matter defined in the appended claims is not necessarily limited to
the described features or acts described above. Rather, the
described features and acts are disclosed as example forms of
implementing the claims.
[0049] Those skilled in the art will appreciate that the disclosure
may be practiced in network computing environments with many types
of computer system configurations, including, an in-dash vehicle
computer, personal computers, desktop computers, laptop computers,
message processors, hand-held devices, multi-processor systems,
microprocessor-based or programmable consumer electronics, network
PCs, minicomputers, mainframe computers, mobile telephones, PDAs,
tablets, pagers, routers, switches, various storage devices, and
the like. The disclosure may also be practiced in distributed
system environments where local and remote computer systems, which
are linked (either by hardwired data links, wireless data links, or
by a combination of hardwired and wireless data links) through a
network, both perform tasks. In a distributed system environment,
program modules may be located in both local and remote memory
storage devices.
[0050] Further, where appropriate, functions described herein can
be performed in one or more of: hardware, software, firmware,
digital components, or analog components. For example, one or more
application specific integrated circuits (ASICs) can be programmed
to carry out one or more of the systems and procedures described
herein. Certain terms are used throughout the description and
claims to refer to particular system components. As one skilled in
the art will appreciate, components may be referred to by different
names. This document does not intend to distinguish between
components that differ in name, but not function.
[0051] It should be noted that the sensor embodiments discussed
above may comprise computer hardware, software, firmware, or any
combination thereof to perform at least a portion of their
functions. For example, a sensor may include computer code
configured to be executed in one or more processors, and may
include hardware logic/electrical circuitry controlled by the
computer code. These example devices are provided herein purposes
of illustration, and are not intended to be limiting. Embodiments
of the present disclosure may be implemented in further types of
devices, as would be known to persons skilled in the relevant
art(s).
[0052] At least some embodiments of the disclosure have been
directed to computer program products comprising such logic (e.g.,
in the form of software) stored on any computer useable medium.
Such software, when executed in one or more data processing
devices, causes a device to operate as described herein.
[0053] While various embodiments of the present disclosure have
been described above, it should be understood that they have been
presented by way of example only, and not limitation. It will be
apparent to persons skilled in the relevant art that various
changes in form and detail can be made therein without departing
from the spirit and scope of the disclosure. Thus, the breadth and
scope of the present disclosure should not be limited by any of the
above-described exemplary embodiments, but should be defined only
in accordance with the following claims and their equivalents. The
foregoing description has been presented for the purposes of
illustration and description. It is not intended to be exhaustive
or to limit the disclosure to the precise form disclosed. Many
modifications and variations are possible in light of the above
teaching. Further, it should be noted that any or all of the
aforementioned alternate implementations may be used in any
combination desired to form additional hybrid implementations of
the disclosure.
* * * * *